Robot learning control based on recurrent neural network inverse model

Abstract

A non-linear model-based feedforward, feedback, and learning controller is presented. This controller can control a non-linear plant such as a robot whose dynamics are initially unknown. In the feedforward part, a recurrent neural network (RNN) is used to model the inverse dynamics of the plant. In the feedback part, a PD controller is added to handle unmodeled dynamics and disturbances. Furthermore, an add-on learning controller is established to reduce tracking errors for repetitive tasks. The controller is validated with the control of a simulated two-joint manipulator. Simulation results show that the controller can successfully learn the inverse dynamics of a robot, perform accurate tracking for a general trajectory, and improve its own performance over the repetitions of a trajectory, with and without a payload change. © 1997 John Wiley & Sons, Inc.